Summary of PRDV420: Introduction to R Programming | Saylor Academy

PRDV420: Introduction to R Programming

Learn the basics of data manipulation and visualization in R – the world’s most popular language for statistical computing – even if you do not have prior programming experience.

Course Introduction

Course Syllabus

Unit 1: Introduction to R and RStudio

1.1: R and Coding Environments

Overview of R

Introduction to R and RStudio

1.2: Installing and Setting Up R and RStudio

Installing R and RStudio

Setting up RStudio

Updating Software

1.3: Command Line and Script

Using R as a Calculator

Practice: Calculator

1.4: Functions and Packages

Functions

Practice: Functions

Packages

Updating R and Its Packages

Practice: Functions and Packages

1.5: Management of Code and Other Files

R Projects and Files in a Project

Practice: R Projects

Best Practices for Writing R Code

Unit 1 Assessment

Unit 1 Assessment

Unit 2: Basic Object Types and Operations in R

2.1: Data Types

Basic Data Types and Data Structures in R

Practice: Data Types

Strings

Practice: Strings

Factors

Practice: Factors

2.2: Vectors

Vectors and Simple Manipulations

Vectors and Type Coercion

Practice: Vectors

2.3: Arrays and Matrices

What is the Difference Between Arrays and Matrices?

Arrays in R

Matrices in R

Practice: Arrays and Matrices

2.4: Lists and Data Frames

Lists and Data Frames

Practice: Base-R Lists and Data Frames

The Tibble Format

Practice: Tibbles

The data.table Format

Practice: Data Tables

Unit 2 Assessment

Unit 2 Assessment

Unit 3: Data Import and Export

3.1: Data Input via Keyboard or Number Generation

Entering Data

Data Sets in Base R

Practice: Built-in Datasets

Pseudo-Random Number Generation

Practice: Random Number Generation

Reproducible Simulations

3.2: Loading External Files

Data Loading and Viewing

Base R: Reading Plain-Text Files

Tidyverse: Reading Plain-Text Files

Practice: read_csv

Parsing a Vector

Practice: Parsing a Vector

Parsing a File

Using the readxl Package to Read Excel Files

Loading Files From Other Programs

3.3: Data Export and Reusing R Data

Saving and Reloading Data in R Format

Practice: Export and Reuse

Base R: Writing to a CSV File

Tidyverse: Writing to a CSV File

Practice: Export to a CSV File

Practice: Data Manipulation in a Project

Unit 3 Assessment

Unit 3 Assessment

Unit 4: Data Visualization

4.1: Base-R and ggplot2 Graphics